From 8aa29810bb77611cc20b7a384897ff6703783ea1 Mon Sep 17 00:00:00 2001 From: Chris Dyer Date: Sun, 18 Nov 2012 13:35:42 -0500 Subject: major restructure of the training code --- minrisk/minrisk_optimize.cc | 197 -------------------------------------------- 1 file changed, 197 deletions(-) delete mode 100644 minrisk/minrisk_optimize.cc (limited to 'minrisk/minrisk_optimize.cc') diff --git a/minrisk/minrisk_optimize.cc b/minrisk/minrisk_optimize.cc deleted file mode 100644 index da8b5260..00000000 --- a/minrisk/minrisk_optimize.cc +++ /dev/null @@ -1,197 +0,0 @@ -#include -#include -#include -#include - -#include -#include - -#include "liblbfgs/lbfgs++.h" -#include "filelib.h" -#include "stringlib.h" -#include "weights.h" -#include "hg_io.h" -#include "kbest.h" -#include "viterbi.h" -#include "ns.h" -#include "ns_docscorer.h" -#include "candidate_set.h" -#include "risk.h" -#include "entropy.h" - -using namespace std; -namespace po = boost::program_options; - -void InitCommandLine(int argc, char** argv, po::variables_map* conf) { - po::options_description opts("Configuration options"); - opts.add_options() - ("reference,r",po::value >(), "[REQD] Reference translation (tokenized text)") - ("weights,w",po::value(), "[REQD] Weights files from current iterations") - ("input,i",po::value()->default_value("-"), "Input file to map (- is STDIN)") - ("evaluation_metric,m",po::value()->default_value("IBM_BLEU"), "Evaluation metric (ibm_bleu, koehn_bleu, nist_bleu, ter, meteor, etc.)") - ("temperature,T",po::value()->default_value(0.0), "Temperature parameter for objective (>0 increases the entropy)") - ("l1_strength,C",po::value()->default_value(0.0), "L1 regularization strength") - ("memory_buffers,M",po::value()->default_value(20), "Memory buffers used in LBFGS") - ("kbest_repository,R",po::value(), "Accumulate k-best lists from previous iterations (parameter is path to repository)") - ("kbest_size,k",po::value()->default_value(500u), "Top k-hypotheses to extract") - ("help,h", "Help"); - po::options_description dcmdline_options; - dcmdline_options.add(opts); - po::store(parse_command_line(argc, argv, dcmdline_options), *conf); - bool flag = false; - if (!conf->count("reference")) { - cerr << "Please specify one or more references using -r \n"; - flag = true; - } - if (!conf->count("weights")) { - cerr << "Please specify weights using -w \n"; - flag = true; - } - if (flag || conf->count("help")) { - cerr << dcmdline_options << endl; - exit(1); - } -} - -EvaluationMetric* metric = NULL; - -struct RiskObjective { - explicit RiskObjective(const vector& tr, const double temp) : training(tr), T(temp) {} - double operator()(const vector& x, double* g) const { - fill(g, g + x.size(), 0.0); - double obj = 0; - double h = 0; - for (unsigned i = 0; i < training.size(); ++i) { - training::CandidateSetRisk risk(training[i], *metric); - training::CandidateSetEntropy entropy(training[i]); - SparseVector tg, hg; - double r = risk(x, &tg); - double hh = entropy(x, &hg); - h += hh; - obj += r; - for (SparseVector::iterator it = tg.begin(); it != tg.end(); ++it) - g[it->first] += it->second; - if (T) { - for (SparseVector::iterator it = hg.begin(); it != hg.end(); ++it) - g[it->first] += T * it->second; - } - } - cerr << (1-(obj / training.size())) << " H=" << h << endl; - return obj - T * h; - } - const vector& training; - const double T; // temperature for entropy regularization -}; - -double LearnParameters(const vector& training, - const double temp, // > 0 increases the entropy, < 0 decreases the entropy - const double C1, - const unsigned memory_buffers, - vector* px) { - RiskObjective obj(training, temp); - LBFGS lbfgs(px, obj, memory_buffers, C1); - lbfgs.MinimizeFunction(); - return 0; -} - -#if 0 -struct FooLoss { - double operator()(const vector& x, double* g) const { - fill(g, g + x.size(), 0.0); - training::CandidateSet cs; - training::CandidateSetEntropy cse(cs); - cs.cs.resize(3); - cs.cs[0].fmap.set_value(FD::Convert("F1"), -1.0); - cs.cs[1].fmap.set_value(FD::Convert("F2"), 1.0); - cs.cs[2].fmap.set_value(FD::Convert("F1"), 2.0); - cs.cs[2].fmap.set_value(FD::Convert("F2"), 0.5); - SparseVector xx; - double h = cse(x, &xx); - cerr << cse(x, &xx) << endl; cerr << "G: " << xx << endl; - for (SparseVector::iterator i = xx.begin(); i != xx.end(); ++i) - g[i->first] += i->second; - return -h; - } -}; -#endif - -int main(int argc, char** argv) { -#if 0 - training::CandidateSet cs; - training::CandidateSetEntropy cse(cs); - cs.cs.resize(3); - cs.cs[0].fmap.set_value(FD::Convert("F1"), -1.0); - cs.cs[1].fmap.set_value(FD::Convert("F2"), 1.0); - cs.cs[2].fmap.set_value(FD::Convert("F1"), 2.0); - cs.cs[2].fmap.set_value(FD::Convert("F2"), 0.5); - FooLoss foo; - vector ww(FD::NumFeats()); ww[FD::Convert("F1")] = 1.0; - LBFGS lbfgs(&ww, foo, 100, 0.0); - lbfgs.MinimizeFunction(); - return 1; -#endif - po::variables_map conf; - InitCommandLine(argc, argv, &conf); - const string evaluation_metric = conf["evaluation_metric"].as(); - - metric = EvaluationMetric::Instance(evaluation_metric); - DocumentScorer ds(metric, conf["reference"].as >()); - cerr << "Loaded " << ds.size() << " references for scoring with " << evaluation_metric << endl; - - Hypergraph hg; - string last_file; - ReadFile in_read(conf["input"].as()); - string kbest_repo; - if (conf.count("kbest_repository")) { - kbest_repo = conf["kbest_repository"].as(); - MkDirP(kbest_repo); - } - istream &in=*in_read.stream(); - const unsigned kbest_size = conf["kbest_size"].as(); - vector weights; - const string weightsf = conf["weights"].as(); - Weights::InitFromFile(weightsf, &weights); - double t = 0; - for (unsigned i = 0; i < weights.size(); ++i) - t += weights[i] * weights[i]; - if (t > 0) { - for (unsigned i = 0; i < weights.size(); ++i) - weights[i] /= sqrt(t); - } - string line, file; - vector kis; - cerr << "Loading hypergraphs...\n"; - while(getline(in, line)) { - istringstream is(line); - int sent_id; - kis.resize(kis.size() + 1); - training::CandidateSet& curkbest = kis.back(); - string kbest_file; - if (kbest_repo.size()) { - ostringstream os; - os << kbest_repo << "/kbest." << sent_id << ".txt.gz"; - kbest_file = os.str(); - if (FileExists(kbest_file)) - curkbest.ReadFromFile(kbest_file); - } - is >> file >> sent_id; - ReadFile rf(file); - if (kis.size() % 5 == 0) { cerr << '.'; } - if (kis.size() % 200 == 0) { cerr << " [" << kis.size() << "]\n"; } - HypergraphIO::ReadFromJSON(rf.stream(), &hg); - hg.Reweight(weights); - curkbest.AddKBestCandidates(hg, kbest_size, ds[sent_id]); - if (kbest_file.size()) - curkbest.WriteToFile(kbest_file); - } - cerr << "\nHypergraphs loaded.\n"; - weights.resize(FD::NumFeats()); - - double c1 = conf["l1_strength"].as(); - double temp = conf["temperature"].as(); - unsigned m = conf["memory_buffers"].as(); - LearnParameters(kis, temp, c1, m, &weights); - Weights::WriteToFile("-", weights); - return 0; -} - -- cgit v1.2.3